Feature Analysis of Eeg Signals Using Som

The electroencephalogram (EEG) represents an efficient technique to measure and record brain electrical activity. The most common use of EEG includes the monitoring and diagnosis of the brain states and their disorders. It is based on the search of characteristic patterns in EEG signals and their evaluation. In terms of signal processing it uses feature analysis, more specifically feature extraction and classification of signal components. The paper deals with the feature study of EEG signals by the self-organizing neural network (SOM). The SOM is an unsupervised method using a neighborhood function to preserve the topological properties of the input space. Resulting algorithm was implemented in MATLAB with many optional parameters that provide possibility to adjust the model to user’s requirements. The graphical user interface was designed as well. General problems of feature analysis, such as extraction of appropriate characteristic features or evaluation of quality of clusters, were also discussed. Presented methodology can be generally applied to each problem requiring feature analysis.